用户可交互探索推荐系统中的算法偏见及其影响。
Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systems
- 通过可视化与反事实解释，让用户直观理解推荐偏差。
- 基于用户访谈设计，支持个性化影响评估。
- 适合关注公平性与透明度的研究者和普通用户。
推荐系统已深度嵌入数字体验，影响用户互动与偏好。尽管广泛应用，这些系统常存在算法偏见，导致不公平且令人不满的用户体验。本研究提出一种交互式工具，帮助用户理解并探索推荐系统中算法危害的影响。该工具结合可视化、反事实解释与交互模块，使用户能够探究诸如校准不足、刻板印象和信息茧房等偏见如何影响其推荐结果。基于深入的用户访谈，该工具不仅提升透明度，还为普通用户与研究人员提供个性化的偏见影响评估，促进对算法偏见的更好理解，助力实现更公平的推荐结果。本研究为未来缓解偏见与增强机器学习算法公平性的研究与应用提供了宝贵洞见。
原文摘要 · Abstract (English)
Recommender systems have become integral to digital experiences, shaping user interactions and preferences across various platforms. Despite their widespread use, these systems often suffer from algorithmic biases that can lead to unfair and unsatisfactory user experiences. This study introduces an interactive tool designed to help users comprehend and explore the impacts of algorithmic harms in recommender systems. By leveraging visualizations, counterfactual explanations, and interactive modules, the tool allows users to investigate how biases such as miscalibration, stereotypes, and filter bubbles affect their recommendations. Informed by in-depth user interviews, this tool benefits both general users and researchers by increasing transparency and offering personalized impact assessments, ultimately fostering a better understanding of algorithmic biases and contributing to more equitable recommendation outcomes. This work provides valuable insights for future research and practical applications in mitigating bias and enhancing fairness in machine learning algorithms.
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